Abstract 2737
Background
Well-differentiated liposarcomas (WDLPS) can be difficult to distinguish from lipomas. Currently, this distinction is made by testing for MDM2-amplification, which requires a biopsy. The aim of this study was to non-invasively predict the MDM2-amplification status using radiomics, i.e. a combination of quantitative imaging features and machine learning techniques, thereby differentiating between lipomas and WDLPS.
Methods
Patients with a lipoma or WDLPS, a known MDM2-amplification status and a pre-treatment T1-weighted MRI scan who were referred to or diagnosed at the Erasmus MC from 2009-2018 were included. When available, other sequences, e.g. T2-weighted (fat-saturated) MRI, were included in the radiomics analysis. Imaging features describing intensity, shape, orientation and texture were extracted from the tumor segmentations; age, gender, tumor depth and tumor localization were added as semantic features. Classification was performed using an ensemble of various machine learning approaches. Evaluation was performed through 100x random-split cross-validation.
Results
We included 116 patients: 58 patients with lipoma and 58 patients with WDLPS. The dataset originated from 41 MRI-scanners, resulting in large heterogeneity in imaging hardware and acquisition protocols. The best performing radiomics approach to differentiate between WDLPS and lipomas, based on T1-weighted imaging only, resulted in a mean [95% confidence interval] AUC of 0.75 [0.65-0.86], accuracy of 0.68 [0.59-0.77], sensitivity of 0.63 [0.48-0.78] and specificity of 0.73 [0.59-0.87]. A model based on the combination of imaging and semantic featured showed only a minor and non-significant improvement in performance.
Conclusions
There is a significant relationship between a radiomics signature, consisting of a combination of quantitative MRI features, and the MDM2-amplification status. Although further optimization and validation is needed for the transition to clinical practice, radiomics has appeared to be a promising, non-invasive approach for the classification of WDLPS and lipomas.
Clinical trial identification
Editorial acknowledgement
M.Vos and M.P.A. Starmans contributed equally to this study.
Legal entity responsible for the study
The authors.
Funding
Stichting Coolsingel, Netherlands Organisation for Scientific Research, Dutch Cancer Society.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
4242 - HIV, HBV and HCV screening practices in oncology: a cross-sectional interregional survey
Presenter: Isabelle Poizot-Martin
Session: Poster Display session 1
Resources:
Abstract
1267 - Genetic landscape of KEAP1 and NFE2L2 mutated cancers from the AACR GENIE database
Presenter: Mark Zaki
Session: Poster Display session 1
Resources:
Abstract
878 - β-arrestin1 is involved in the Ras-induced malignant transformation
Presenter: Takashi Shibano
Session: Poster Display session 1
Resources:
Abstract
4143 - Incidence of second cancer among PLWHIV: retrospective observational study of a series of 601 patients in the French CANCERVIH network
Presenter: Jean-Philippe Spano
Session: Poster Display session 1
Resources:
Abstract
5145 - A challenging task – Identifying carcinoma of unknown primary (CUP) patients according to ESMO guidelines: the CUPISCO trial experience
Presenter: Chantal Pauli
Session: Poster Display session 1
Resources:
Abstract
1737 - Incidence and Outcome of chronic lymphocytic leukemia with Deletion 17p: An Indian experience; challenges and opportunities
Presenter: Ajay Gogia
Session: Poster Display session 1
Resources:
Abstract
2596 - Driving solo? Investigation into collaborating mutations in SDH-deficient neoplasia
Presenter: Jonathan Killian
Session: Poster Display session 1
Resources:
Abstract
1499 - The potential of a novel antiangiogenic VEGFR1-D2 binding peptide in oncology therapeutics
Presenter: Afsaneh Sadre Momtaz
Session: Poster Display session 1
Resources:
Abstract
1775 - First-in-human phase I study of TAS-117, an allosteric AKT inhibitor, in patients with advanced solid tumors
Presenter: Mayu Yunokawa
Session: Poster Display session 1
Resources:
Abstract
4584 - First-in-human study of ABBV-621 in patients (pts) with previously treated sold tumors: Dose-optimization cohorts
Presenter: Emiliano Calvo
Session: Poster Display session 1
Resources:
Abstract